Modelling Protein-Protein Interactions to Elucidate Molecular Mechanisms Behind Neurodegenerative Diseases

You are cordially invited to attend the PhD Dissertation Defense of Gianvito GRASSO on Wednesday, December 12, 2018 at 14h30 in room SI-013 (Informatics building)

Abstract
The worldwide significant increase in life expectancy has recently drawn the attention of the scientific community to neurodegenerative pathologies of the elderly population. These neurodegenerative disorders arise from the abnormal protein aggregation in the nervous tissue leading to intracellular inclusions or extracellular aggregates in specific brain areas. A feasible strategy to prevent the resulting neurodegeneration is based on the development of anti-amyloid molecules, i.e., those capable of preventing the generation of toxic aggregates. To address this issue, it’s extremely important to shed light on the molecular interactions responsible for protein aggregation. Despite substantial research efforts in this field, the fundamental mechanisms of protein misfolding and aggregation mechanisms remain somewhat unrevealed. In this context, computational molecular modelling represents a powerful tool in connecting macroscopic experimental findings to nanoscale molecular events.

The present PhD thesis focuses on the application of computational methodologies to investigate molecular features of protein-protein interactions responsible for two different pathologies: Spinocerebellar Ataxia Type-1 (SCA1) and Alzheimer’s Disease (AD). To address this goal, molecular dynamics simulations have been employed to elucidate the early stages of protein aggregation mechanism at molecular level. From the computational point of view, insufficient sampling often limits the ability of computer simulations to investigate the conformational properties of biomacromolecules. The limitation mainly results from proteins’ rough energy landscapes, with many local minima separated by high-energy barriers. Within this framework, one of the main challenges of MD simulations is the ability to sample experimentally relevant millisecond to second timescales. However, the time-scale of the classical MD simulations with atomic resolution is today limited to few μs. In this regard, enhanced sampling methods represent a powerful tool to improve the sampling efficiency of classical MD, including those that artificially add an external driving force to guide the protein from one structure to another. The present PhD work benefits from the application of enhanced sampling techniques and dimensionality reduction methodologies to elucidate the aggregation pathway of the Ataxin-1 and Amyloid Beta assembly, responsible for SCA1 and AD, respectively. Outcome of the present research represents an important piece of knowledge to design small molecules able to inhibit the protein-protein interactions leading to aggregation. On the other hand, fine tuning of the interatomic forces responsible for the intriguing mechanical properties of the amyloid fibrils is a crucial breakthrough to support the rational design of amyloid-inspired nanostructures as novel biomaterials.

Dissertation Committee

• Prof. Rolf Krause, Università della Svizzera italiana, Switzerland (Research Advisor)
• Prof. Andrea Danani, Dalle Molle Institute for Artificial Intelligence Studies, Switzerland (Co-Advisor)
• Prof. Luca Maria Gambardella, Università della Svizzera italiana, Switzerland (Internal Member)
• Prof. Michele Parrinello, Università della Svizzera italiana, Switzerland (Internal Member)
• Prof. Jack Tuszynski, Department of Oncology University of Alberta, Canada (External Member)
• Prof. Umberto Morbiducci, Politecnico di Torino, Italy (External Member)